To prevent complication caused by moyamoya disease from threatening patients’ lives， timely and effective diagnosis of moyamoya disease is needed. An improved Faster RCNN algorithm for moyamoya disease detection is presented. Firstly， the digital subtraction angiography （DSA） image of internal carotid artery is extracted and enhanced. The ratio of training set， verification set and test set is 6∶2∶2. ResNet101 network is used as the feature extraction network to avoid blurring or loss of vascular features in the process of convolution and pooling. Combined with region proposal network （RPN）， the location of moyamoya disease focus is located. Then replace ROI pooling in Faster RCNN model with ROI Align for feature mapping to avoid the error impact caused by quantization. The average precision （AP） is used as the evaluation index of the detection performance of the algorithm. The AP of normal samples and moyamoya disease samples are 99.23% and 89.39%， respectively. Experimental results show that the proposed method can realize the rapid and effective detection of moyamoya disease. It can accurately detect the location of moyamoya disease lesions in the complex vascular network， and provide some technical support for the auxiliary diagnosis of moyamoya disease.